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Sex, mutation and fitness: asymmetric costs and routes to recovery through compensatory evolution

2005· article· en· W2085439178 on OpenAlexafffund
Alison Pischedda, Adam K. Chippindale

Bibliographic record

VenueJournal of Evolutionary Biology · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologySexual selectionGeneticsPopulationSelection (genetic algorithm)Evolutionary biologyExperimental evolutionGenetic FitnessInbreeding depressionMutationReproductive successMutation AccumulationMutation rateBiological evolutionDemographyGene

Abstract

fetched live from OpenAlex

A wild-type population of Drosophila melanogaster was used to assess the impact of a known deleterious mutation, nub(1), when it had (1) evolved for up to 180 generations with the mutation or (2) recently had the same mutant allele introgressed into it. Relative to this benchmark, we observed much stronger initial fitness depression in males (-74%) than in females (-38%) and also relatively greater fitness recovery by evolved males (+55%) than females (+17%). Experimental assays revealed amelioration in both juvenile and adult fitness and suggested that the greater relative recovery of male fitness was from gains through sexual selection. These evolutionary changes in male fertility depended on pairing with their coevolved mates for both mate choice and post-copulatory components of sexual selection. Without replication at the population level, these results are used to motivate a general hypothesis rather than definitively test it: Differences in reproductive optima may generally skew mutational effects towards the more strongly sexually-selected sex due to genic capture and condition dependence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.254
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations39
Published2005
Admission routes2
Has abstractyes

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